Vondrák Martin, Reuter Karsten, Margraf Johannes T
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany.
J Chem Phys. 2023 Aug 7;159(5). doi: 10.1063/5.0156290.
Many state-of-the art machine learning (ML) interatomic potentials are based on a local or semi-local (message-passing) representation of chemical environments. They, therefore, lack a description of long-range electrostatic interactions and non-local charge transfer. In this context, there has been much interest in developing ML-based charge equilibration models, which allow the rigorous calculation of long-range electrostatic interactions and the energetic response of molecules and materials to external fields. The recently reported kQEq method achieves this by predicting local atomic electronegativities using Kernel ML. This paper describes the q-pac Python package, which implements several algorithmic and methodological advances to kQEq and provides an extendable framework for the development of ML charge equilibration models.
许多先进的机器学习（ML）原子间势基于化学环境的局部或半局部（消息传递）表示。因此，它们缺乏对长程静电相互作用和非局部电荷转移的描述。在这种背景下，人们对开发基于ML的电荷平衡模型非常感兴趣，该模型能够严格计算长程静电相互作用以及分子和材料对外部场的能量响应。最近报道的kQEq方法通过使用核机器学习预测局部原子电负性来实现这一点。本文描述了q-pac Python软件包，它对kQEq实现了多项算法和方法上的改进，并为ML电荷平衡模型的开发提供了一个可扩展的框架。
